Meet Kegan Maher. Kegan is the Data Officer for the City of Santa Monica. He moved to Santa Monica 15 years ago from Seattle and has been involved in City government basically ever since.
How’d you get into data?
My brother showed me Excel when I was five or six. He showed me how to calculate my age in minutes and seconds, and I was hooked. I went on to study Math at UCLA while interning with the City of Santa Monica. After graduating, they hired me full-time to do web development, just as the “Open Data” movement began sweeping local government. We were well positioned to participate, and made our own unique mark by releasing an open-source code library for publishing data online -- to date, it’s still the most popular library for the target technology stack.
What are your go-to analysis and visualization tools?
Lately I’ve been working primarily in Excel. I want to use a relatively familiar “no code” tool, so I can share techniques with other, less-technical colleagues. Opening the data is just half of the story -- you have to get people to do something with it. And so a lot of my work recently has been around showing just how much you can do to tidy and analyze your data just using out of the box Excel.
Visualization has been a different challenge, but lately we’ve been having a lot success with a dashboarding product called SiSense. We’re able to quickly build up interactive dashboards, while also giving our end-users the ability to do the same with relatively little training.
What issue in Los Angeles do you think has the most potential for a data-driven solution?
Transportation. It may be a little naive to think we can build our way up and out of the sprawl that is Los Angeles (though the region certainly does NEED more housing). I don’t own a car and I’m a big supporter of public transportation. Obviously the reality is, there will always be some folks that want to or need to drive -- but I think there are solutions out there waiting to be tapped. Solutions for how to make “green commuting” a more reliable and efficient method. Solutions for how to connect the far-flung neighborhoods and cities of LA County. Solutions to help folks understand the longer-term personal and community wellbeing benefits of getting out of our cars.
What’s your favorite “data-story”?
The opening of the Big Blue Bus data. When it comes to bus data, as a rider, two things matter: the official bus schedule and a given bus’ current position (and therefore, its schedule adherence). We provide both types of data in GTFS format, a data standard developed in partnership by the Portland, OR regional transit agency (TriMet), and Google. Services like Google Maps, Transit App, Moovit, and others are then able to show bus arrival data in real time. As anyone who has ever taken a bus can confirm: waiting can often be the most painful part of the experience, especially when the wait time is as long as it takes for the bus to arrive. With real-time arrival data in the palm of your hand, you get to decide to have one more sip of coffee or to finish up one more email, before running out to the bus stop. Whether the bus is “late” or not becomes less important, as long as you and the bus arrive at the stop more-or-less together.
What advice do you have for someone looking to start using LA Counts datasets to tell their own stories?
There are two ways to approach data-storytelling. On the one hand, starting with a story in mind and trying to find supporting data. This can be challenging and may lead down an unexpected path that may or may not be related to the original idea. On the other hand, coming in to some data with an open mind and starting to explore can generate fresh ideas and more questions for further investigation. The former approach may be easier for a journalist or someone more used to storytelling, but the data to support a given story might not exist or may be challenging to acquire or work with. The latter approach definitely requires more technical expertise to start to understand the patterns and information hidden away within some given data. LA Counts might function as a hub to make both of these techniques more readily accessible to a wider audience.